The dead-end elimination algorithm (DEE) is a method for minimizing a function over a discrete set of independent variables. The basic idea is to identify "dead ends", i.e., "bad" combinations of variables that cannot possibly yield the global minimum and to refrain from searching such combinations further. Hence, dead-end elimination is a mirror image of dynamic programming, in which "good" combinations are identified and explored further. Although the method itself is general, it has been developed and applied mainly to the problems of predicting and designing the structures of proteins. The original description and proof of the dead-end elimination theorem can be found in .
Other articles related to "elimination":
... One example is a refinement of the singles elimination criterion known as the Goldstein criterion, which arises from fairly straightforward algebraic manipulation before applying the minimization Thus rotamer ...
Famous quotes containing the words elimination, dead-end:
“To reduce the imagination to a state of slaveryeven though it would mean the elimination of what is commonly called happinessis to betray all sense of absolute justice within oneself. Imagination alone offers me some intimation of what can be.”
—André Breton (18961966)
“Corner a dog in a dead-end street and it will turn and bite.”